Understanding the complex spatial and temporal process by which tumors initiate, evolve and respond to therapy is a major focus of the oncology community and one that requires the integration of multiple disciplines. A diverse suite of therapies have been developed in the modern era, leading to significantly improved survival rates across many cancers. However, many treatments share a cycle of short-term success followed by recurrence, often of a more aggressive tumor. In the past decade, the cancer research community has begun to acknowledge the importance of heterogeneity across genotypic, phenotypic, and environmental scales as a key driver in drug resistance and treatment failure. The intricate dialogue between tumor cells and environment selects for clones that are best adapted phenotypically to survive, regardless of specific mutations that may facilitate tumor progression. These dynamics, occurring between a heterogeneous tumor and a heterogeneous environment (the cancer ecosystem) are almost impossible to dissect experimentally. Further, adding multiple treatments to the mix often leads to nonlinear and unintuitive dynamics. Therefore, understanding how tumor evolution and ecology changes with treatment is key to controlling the emergence of aggressive and resistant clones following therapy. Our central hypothesis here is that when treating cancer we should exploit heterogeneity, rather than ignore it, by developing crowdsourced sequential and combination therapies that steer tumor evolution and ecology producing more effective, less toxic and longer lasting responses. We plan to test this hypothesis through the development of a research game based on treating a heterogeneous evolving cancer. The core engine of the game will be a calibrated mathematical model of solid tumor growth, tailored to specific organ sites through different associated tumor phenotypes, environment and treatment options. Based on patterns observed while interacting with our research game, successful players will choose the follow-up treatments based on an understanding of the cancer's adaptive response to previous treatments as well as how the cancer is responding to the current therapy in real time. As a result of the power of crowdsourced computation and human intelligence we will derive a suite of optimal treatment strategies across a diverse set of cancer ecosystems.